Fit for Practice? : Reflections on integrating interactive machine learning within in-clinic physiotherapy

Abstract: Interactive machine learning (IML) is a promising approach for personalising physiotherapy training. This thesis uses a research- through-design and reflective approach to explore how IML can be ecologically integrated within in-clinic physiotherapy. Domain expert interviews and observations with physiotherapists were conducted to gain a broader understanding of the physiotherapy context, the role of feedback provided to patients, and how technology could be integrated into this context. Three design artefacts were proposed to participants to provoke a discussion on the implications and current practices. Due to time constraints, the findings suggest incorporation within the consultation may be difficult. The clinic’s gym or administration time revealed to be promising alternatives. Furthermore, results highlight the importance of IML supporting richer interaction forms and the implicitness and flexibility needed to describe movement and feedback, which define physiotherapy’s hands-on approach. Togetherness was a reoccurring theme, suggesting that the input and guidance of the IML system could be something done with the patient. Finally, a reflection on the results and the study opens up a discussion of the fitness of IML in physiotherapy.